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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import utm\n",
    "\n",
    "def gps2utm(df):\n",
    "    easting, northing, *_ = utm.from_latlon(np.array(df['lat']), np.array(df['lon']))\n",
    "    return (easting, northing)\n",
    "\n",
    "datasets = ['dataset%i' % i for i in range(1, 4)]\n",
    "\n",
    "_, ax = plt.subplots()\n",
    "for dataset in datasets:\n",
    "    df = pd.read_csv(dataset + '/coordinates.csv')\n",
    "    ax.plot(*gps2utm(df))\n",
    "\n",
    "ax.axis('equal')\n",
    "ax.set_xlabel('Easting (m)')\n",
    "ax.set_ylabel('Northing (m)')\n",
    "ax.legend(datasets);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}